VQ-VAE Fisheye Image Rectification Network
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Solution Overview
Problem
Existing image processing systems, particularly those using deep neural networks, face challenges in efficiently rectifying fisheye images acquired with ultra-wide-angle lenses, as they require laborious analytical model calibration and consume significant computing resources, which is error-prone and inefficient.
Innovation Solution
The use of a vector quantized variational autoencoder (VQ-VAE) to rectify fisheye images by learning from sample data, either in supervised or unsupervised modes, eliminating the need for analytical model calibration and reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical model calibration is used for fisheye image rectification, then rectification accuracy can be achieved, but the process becomes laborious and error-prone due to trial-and-error calibration
Solution Approach 1:
The patent replaces the manual trial-and-error calibration process with an automated deep learning-based rectification network. The system learns optimal rectification parameters automatically from training data, eliminating the need for laborious analytical model calibration while maintaining high rectification accuracy.
Solution Approach 2:
The patent performs preliminary training of the rectification network using paired fisheye and rectilinear images during the offline phase. This preliminary action pre-computes the optimal rectification transformations, so that during online operation, only simple inference is needed without repeated calibration trials.
2Productivity
If traditional deep neural networks are used for image transformation, then object detection can be performed, but significant computing resources are consumed
Solution Approach 1:
The patent segments the image processing task into two distinct stages: (1) a lightweight rectification network that transforms fisheye images to rectilinear format, and (2) existing object detection networks that process the rectified images. This segmentation allows each component to be optimized independently, reducing overall computational burden.
Solution Approach 2:
The patent introduces rectified rectilinear images as an intermediary representation between fisheye input and object detection output. This intermediate format enables standard object detection algorithms to work effectively on fisheye images without requiring them to be computationally adapted to handle distortion directly.
3Shape
If fisheye images are rectified using conventional methods, then rectilinear format is achieved, but the process is inefficient and time-consuming
Solution Approach 1:
The patent replaces conventional analytical rectification methods with a deep learning-based rectification network. This network learns the complex non-linear transformations required for fisheye rectification and applies them in a single forward pass, dramatically reducing processing time compared to iterative analytical methods.
Solution Approach 2:
The patent performs preliminary training of the rectification network offline using large datasets of paired fisheye and rectilinear images. Once trained, the network can rapidly rectify new images in real-time without requiring repeated computational iterations, thus reducing online processing time.
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to input a fisheye image to a vector quantized variational autoencoder. The vector quantized variational autoencoder can encode the fisheye image to first latent variables based on an encoder. The vector quantized variational autoencoder can quantize the first latent variables to generate second latent variables based on a dictionary of embeddings. The vector quantized variational autoencoder can decode the second latent variables to a rectified rectilinear image using a decoder and output the rectified rectilinear image.